Introduction to Reinforcement Learning
author:
Csaba Szepesvari,
Department of Computing Science, University of Alberta
Description
The tutorial will introduce
Reinforcement Learning, that is, learning what actions to take,
and when to take them, so as to optimize long-term performance. This may
involve sacrificing immediate reward to obtain greater reward in the
long-term or just to obtain more information about the environment. The
first part of the tutorial will cover the basics, such as Markov
decision processes, dynamic programming, temporal-difference learning,
Monte Carlo methods, eligibility traces, the role of function
approximation. In the second part we cover some recent developments,
namely policy gradient and second order methods, such as LSPI and the
modified Bellman residual minimization algorithm.
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Part 2
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Part 3
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Part 5
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Part 6
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